In this paper, we address the recommendation process as a one-class classification problem based on content features and a Negative Selection (NS) algorithm that captures user preferences. Specifically, we develop an Artificial Immune System (AIS) based on a Negative Selection Algorithm that forms the core of a music recommendation system. A NS-based learning algorithm allows our system to build a classifier of all music pieces in a database and make personalized recommendations to users. This is achieved quite efficiently through the intrinsic property of the NS algorithm to discriminate “self-objects” (i.e. music pieces of user's like) from “non self-objects”, especially when the class of non self-object is vast when compared to the class of self-objects and the examples (samples) of music pieces come only from the class of self-objects (music pieces of user's like). Our recommender system has been fully implemented and evaluated and found to outperform state of the art recommender systems based on support vector machines-based methodologies.